Industrial and power generation turbines have control systems (“controllers”) that monitor and control their operation. These controllers govern the combustion system of the turbine and other operational aspects of the turbine. The controller may execute scheduling algorithms that adjust the fuel flow, inlet guide vanes (IGV) and other control inputs to ensure safe and efficient operation of the turbine.
Turbine controllers may receive input values of measured operating parameters and desired operating settings, that in conjunction with scheduling algorithms, determine settings for control parameters to achieve the desired operation. The values prescribed by the scheduling algorithms for the control parameters may cause the turbine to operate at a desired state, such as at a desired power output level and within defined emission limits. The scheduling algorithms incorporate assumptions regarding the turbine, such as that it is operating at a certain efficiency, with a certain flow capacity and at other assumed conditions.
As the turbine operates for an extended period, component efficiencies tend to degrade, and flow capacities and other operating conditions may vary from the assumed conditions. Because of this deterioration, the control scheduling algorithms may become increasingly out of tune and cause the turbine to operate at states that increasingly diverge from the desired operational state.
In response, turbine control systems have been developed that employ adaptive turbine models to estimate certain operating parameters of an operating turbine. The models may estimate operational parameters that are not directly sensed (or measured) by sensors for use in control algorithms. The models may also estimate operational parameters that are measured so that the estimated and measured conditions can be compared. The comparison may be used to automatically tune the model while the turbine continues to operate.
Accordingly, a turbine controlled by an adaptive turbine model may experience operational efficiencies by utilizing a model that is automatically tuned to the actual operating conditions of the turbine, thus allowing the control of the turbine to directly reflect the state of the turbine and operate efficiently and/or to its maximum thresholds. These operational efficiencies may produce significant benefits over the turbine operating under or based on the legacy control system, though it may be difficult to quantify the benefits realized without a means to compare the turbine operating based on the new control system including an adaptive turbine model to the turbine, in its present state, as it would be operating based on a legacy control system.
Furthermore, an adaptive turbine model, because of its ability to be tuned real-time, or in near-real-time, to reflect the operational state, and because of the flexibility it provides for controlling the turbine, may be useful to predict the turbine operation under operating conditions and control strategies and strategies different than those that exist for the turbine. Doing so may allow for determining an alternate control strategy.
However, to accomplish analyzing the turbine based on an alternate control strategy, while the turbine is in operation based on another control strategy, for example an upgraded control system, or an actual control strategy given the desired goals, ambient conditions, or system status, current control systems utilizing a single turbine model may not be used because the model is responsive to the control parameters and/or control outputs of actual operation of the turbine.
Thus, there exists a need for a control system including an alternate adaptive model that is tuned to the operating conditions and status of the turbine but where the alternate control system may be analyzed based on a control strategy different than that used for actual turbine control.
Further, there is a need for methods and systems for providing real-time comparison with an alternate control strategy for a turbine.